Packages

class KMeansModel extends Model[KMeansModel] with KMeansParams with GeneralMLWritable with HasTrainingSummary[KMeansSummary]

Model fitted by KMeans.

Annotations
@Since( "1.5.0" )
Source
KMeans.scala
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. KMeansModel
  2. HasTrainingSummary
  3. GeneralMLWritable
  4. MLWritable
  5. KMeansParams
  6. HasMaxBlockSizeInMB
  7. HasSolver
  8. HasWeightCol
  9. HasDistanceMeasure
  10. HasTol
  11. HasPredictionCol
  12. HasSeed
  13. HasFeaturesCol
  14. HasMaxIter
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val distanceMeasure: Param[String]

    Param for The distance measure.

    Param for The distance measure. Supported options: 'euclidean' and 'cosine'.

    Definition Classes
    HasDistanceMeasure
  2. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  3. final val k: IntParam

    The number of clusters to create (k).

    The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  4. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  5. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  6. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  7. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  8. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol

Members

  1. final def clear(param: Param[_]): KMeansModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def clusterCenters: Array[Vector]
    Annotations
    @Since( "2.0.0" )
  3. def copy(extra: ParamMap): KMeansModel

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    KMeansModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  4. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  5. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  8. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  9. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  10. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  11. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  12. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  13. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  14. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  15. def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Definition Classes
    HasTrainingSummary
    Annotations
    @Since( "3.0.0" )
  16. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  17. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  18. lazy val numFeatures: Int
    Annotations
    @Since( "3.0.0" )
  19. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  20. var parent: Estimator[KMeansModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  21. def predict(features: Vector): Int
    Annotations
    @Since( "3.0.0" )
  22. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  23. final def set[T](param: Param[T], value: T): KMeansModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  24. def setParent(parent: Estimator[KMeansModel]): KMeansModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  25. def summary: KMeansSummary

    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if hasSummary is false.

    Definition Classes
    KMeansModel → HasTrainingSummary
    Annotations
    @Since( "2.0.0" )
  26. def toString(): String
    Definition Classes
    KMeansModelIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  27. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    KMeansModelTransformer
    Annotations
    @Since( "2.0.0" )
  28. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  29. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  30. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    KMeansModelPipelineStage
    Annotations
    @Since( "1.5.0" )
  31. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    KMeansModelIdentifiable
    Annotations
    @Since( "1.5.0" )
  32. def write: GeneralMLWriter

    Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.

    For KMeansModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    Definition Classes
    KMeansModelGeneralMLWritableMLWritable
    Annotations
    @Since( "1.6.0" )

Parameter setters

  1. def setFeaturesCol(value: String): KMeansModel.this.type

    Annotations
    @Since( "2.0.0" )
  2. def setPredictionCol(value: String): KMeansModel.this.type

    Annotations
    @Since( "2.0.0" )

Parameter getters

  1. final def getDistanceMeasure: String

    Definition Classes
    HasDistanceMeasure
  2. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  3. def getK: Int

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  4. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  5. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  6. final def getSeed: Long

    Definition Classes
    HasSeed
  7. final def getSolver: String

    Definition Classes
    HasSolver
  8. final def getTol: Double

    Definition Classes
    HasTol
  9. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val initMode: Param[String]

    Param for the initialization algorithm.

    Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  2. final val initSteps: IntParam

    Param for the number of steps for the k-means|| initialization mode.

    Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  3. final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Definition Classes
    HasMaxBlockSizeInMB
  4. final val solver: Param[String]

    Param for the name of optimization method used in KMeans.

    Param for the name of optimization method used in KMeans. Supported options:

    • "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row".
    • "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training.
    • "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".
    Definition Classes
    KMeansParams → HasSolver
    Annotations
    @Since( "3.4.0" )

(expert-only) Parameter getters

  1. def getInitMode: String

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  2. def getInitSteps: Int

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  3. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB